Neural networks are becoming increasingly important in many areas of adaptive signal processing and pattern recognition (D. E. Rumelhart, J. L. McClelland, Parallel Distributed Processing, Vols. I and II, MIT Press, Cambridge, Mass., 1986). There are different types of neural networks adapted to different tasks. Purely forward-coupled ADALINE-type neural networks are preferably used in pattern recognition (B. Widrow, R. G. Winter, R. A. Baxter, "Layered neural nets for pattern recognition", IEEE Trans. on ASSP, Vol. ASSP-36, No. 7, pp. 1109-1118, 1988).
The basic element of the ADALINE network is the "adaptive linear neuron" (ADALINE). This is understood to be a linear combiner followed by a sign decision means. The linear combiner generates a weighted sum from N input signals whose sign is determined by the sign decision means (single-stage quantizer) and is output as the output signal of the ADALINE (P. Strobach, "A neural network with Boolean Output Layer", Proc. IEEE Int. Conf. on ASSP, Albuquerque, N. Mex., April 1990).
By connecting such ADALINE neurons in series, ADALINE networks are obtained which--depending on the values of the weighting factors of the linear combiners --assign binary output signals to the signals applied to the free input nodes of the network.
These binary output signals are interpreted as a classification of the input signals. The input signals may be taken here from binary values, discrete multi-valued values, or an interval of continuous values. In order to be able to set a desired classification of the input signals by means of the neural network, the values of the weighting factors must be quasi-continuously variable. In a training process to determine the wanted values of the weighting factors, the latter are varied, with the aim of minimizing a measure for the deviation between the actual output signals of the network and the desired output signals (nominal responses) for a set of training data, until the actual responses correspond sufficiently precisely to the nominal responses.
For this reason, all known realizations of neural networks are designed either with the aid of analog circuits (P. Muller et al, "A Programmable Analog Neural Computer and Simulator", Adv. Neural Inform. Proc. Systems 1, D. S. Touretzky (ed.), Morgan Kaufmann Publ., San Mateo, Calif., 1989, pp. 712-719) or with the aid of digital circuits with a high word width (N. M. Allinson, "Digital Realization of Self-Organizing Maps", Adv. Neural Inform. Proc. Systems 1, D. S. Touretzky (ed.), Morgan Kaufmann Publ., San Mateo, Calif., 1989, pp. 728-738). In the latter, the continuous input signals and weighting factors are approximated by a large number of discrete stages which are represented by binary variables with a large word width. In all known realizations of ADALINE networks the linear combiners require a very high outlay for circuitry, which hinders the hardware realization of neural networks with comparatively large numbers of neurons.